Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches

Abstract

Social media video sponsorship, where sponsors partner with content creators to promote their brands, has become increasingly popular. Leveraging rich data on sponsored and non-sponsored videos on Facebook, we introduce two metrics, Content Similarity and Audience Closeness, to help sponsors find effective creator-sponsor matches. Content Similarity captures the thematic alignment between a creator’s content and a sponsor’s brand, while Audience Closeness measures how closely aligned a creator’s and sponsor’s audiences are within a social media network. We find that both metrics significantly increase video viewership, but their effects vary over time. Audience Closeness generates more immediate, short-term engagement, while Content Similarity drives sustained, long-term engagement. We additionally examine how congruency metrics can complement other sponsorship strategies, including partnering with more established creators, forming new partnerships, and managing the sponsor's visibility within content. Our findings suggest that Content Similarity can mitigate the negative effects of frequent sponsor appearances and enhance viewer engagement even in repetitive partnerships. Moreover, we find that Audience Closeness is particularly beneficial for larger, more established creators, while Content Similarity is less sensitive to the size of a creator’s following. Finally, we demonstrate how intermediary platforms can adopt these metrics to reduce information asymmetry and improve creator-sponsor matching, especially for long-tail creators and smaller brands.

Keywords: Social Media Video Sponsorship; Content Creator; Sponsor; Sponsorship Relevance; Natural Language Processing; Latent Dirichlet Allocation; Machine Learning; Matrix Completion, influencer marketing, video content analysis, sponsorship relevance

JEL Classification: M31; M37